Quantum Leap Africa (QLA) offers career opportunities in administration and research. Information on current openings and how to apply may be obtained by clicking the position of interest.

FEATURED

Visiting Researcher

Quantum Leap Africa (QLA) is a centre of scientific research excellence at leading edge of information science and technology. The centre with pleasure hosts researchers from all fields and all levels of expertise in Data Science, Smart System Engineering and Quantum Information Science.

Quantum Leap Africa (QLA) is a centre of scientific research excellence at leading edge of information science and technology. The centre with pleasure hosts researchers from all fields and all levels of expertise in Data Science, Smart System Engineering and Quantum Information Science.

In partnership with the Carnegie Corporation of New York and the African Institute for Mathematical Sciences (AIMS), the Quantum Leap Africa Research Centre is pleased to invite promising African PhD students who are at an advanced stage of their research, and researchers with at least a PhD qualification in the field of data science and its related disciplines, to apply for the AIMS-Carnegie Small Research Grants in Data Science and its Applications.

In partnership with the Carnegie Corporation of New York, the African Institute for Mathematical Sciences (AIMS) is offering promising emerging African data scientists of at least postdoctoral standing or who completed their doctorate less than ten years ago, attractive career opportunities at its pioneering Quantum Leap Africa (QLA) Research Centre in Rwanda.

In support of the QLA project, we are in search of a smart, enthusiastic individual to join the team as its first Data Analytics & Security Specialist. Inspired by an ambitious mandate, and led by the Project Developer, you will be play an active role as the team works to build and maintain analytical models that mine large sets of structured, semi-structured and unstructured data in search of unique insights and correlations not evident through traditional Data Warehouse or Business Intelligence techniques.